Modification of the ART-1 architecture based on category theoretic design principles

Many studies have addressed the knowledge representation capability of neural networks. A recently-developed mathematical semantic theory explains the relationship between knowledge and its representation in connectionist systems. The theory yields design principles for neural networks whose behavioral repertoire expresses any desired capability that can be expressed logically. In this paper, we show how the design principle of limit formation can he applied to modify the ART-1 architecture, yielding a discrimination capability that goes beyond vigilance. Simulations of this new design illustrate the increased discrimination ability it provides for multi-spectral image analysis.